The impact of social segregation on human mobility in developing and industrialized regions

Abstract

This study leverages mobile phone data to analyze human mobility patterns in a developing nation, especially in comparison to those of a more industrialized nation. Developing regions, such as the Ivory Coast, are marked by a number of factors that may influence mobility, such as less infrastructural coverage and maturity, less economic resources and stability, and in some cases, more cultural and language-based diversity. By comparing mobile phone data collected from the Ivory Coast to similar data collected in Portugal, we are able to highlight both qualitative and quantitative differences in mobility patterns -such as differences in likelihood to travel, as well as in the time required to travel -that are relevant to consideration on policy, infrastructure, and economic development. Our study illustrates how cultural and linguistic diversity in developing regions (such as Ivory Coast) can present challenges to mobility models that perform well and were conceptualized in less culturally diverse regions. Finally, we address these challenges by proposing novel techniques to assess the strength of borders in a regional partitioning scheme and to quantify the impact of border strength on mobility model accuracy.

Data from mobile phone operators: A tool for smarter cities?

“…Studies that investigate the characteristic spatio-temporal pattern of the collective human mobility from a more dynamic perspective, and make a comparison between different urban environments, are very rare. For example, previous work has shown a significant differences between cities (areas) along metrics such as: commute distances (Isaacman et al, 2010(Isaacman et al, , 2011a(Isaacman et al, , 2011bBecker et al, 2013); commuting patterns (Amini et al, 2014) mobility patterns (Liu et al, 2009;Isaacman et al 2011b;Calabrese et al, 2011a;Kang et al, 2012;Tanahashi et al, 2012;Amini et al, 2014); community structures (Eagle et al, 2009b;Amini et al, 2014), hotspots (Louail et al, 2014), and population density (Martino et al, 2010;Becker et al, 2013;Csáji et al, 2012;Isaacman et al, 2012;Sagle et al, 2012;Yuan and Raubal, 2012). The findings of such studies can be helpful for policy makers in understanding the characteristics and dynamic nature of different urban areas, as well as updating environmental and (public) transportation policies.…”

Tourism Geography: Emerging Trends and Initiatives to Support Tourism in Morocco

“…Since 2006, a number of mobile-phone data case studies have been initiated to analyse human mobility patterns (Eagle and Pentland, 2006;Mateos, 2006;Shoval, 2007;González et al, 2008;Liu et al, 2009;Song, et al, 2010aSong, et al, , 2010bHuang et al, 2010;Isaacman et al 2011bIsaacman et al , 2012Calabrese et al, 2011;Kang et al, 2012;Tanashia et al, 2012;Amini et al, 2014). Huang et al (2010) stated that these places and the routes between them are of significant value to effective network management, public transportation planning and city management.…”

Deriving human activity from geo-located data by ontological and statistical reasoning

“…For instance, whenever a mobile phone call, monetary transaction, or social media post is made, geo-located data is automatically generated by mobile network provider, bank, or social network provider (e.g., Facebook or Twitter) and attached to the data record generated by the activity. An extensive body of works leverage for studying human dynamics through cell phone data [1,25,27,48,60], social media posts [28,41], bank card transactions [61,62] and vehicle GPS traces [32,56]. Over the last few years, the use of mobile phones as sensors of human behavior has radically increased.…”

“…From them, it is possible to infer with a certain level of accuracy, the activities humans are performing at every moment they are connected to the mobile network. Indeed, experiments in large-scale social dynamics have been conducted in the areas of public safety and emergency management [4,36,43], health and disease management [21,66], social and economic development [1,18,22], transport/infrastructure [5,32], urban planning [3,9,24,27,33,38,44] and international development, poverty [59] and more [7]. A large fraction of mobile phone data has been shown to be extremely useful for humanitarian and development applications (Robert Kirkpatrik UN 2013).…”